taptoken 0.1.1

Time series tokenization and detokenization library using tapper patterns.

TSTokenizer

Time series tokenization library. Converts numeric sequences into discrete tokens composed of a spatial pattern (a tapper-pattern ID on a 5×5 grid) and a magnitude vector (absolute deltas, optionally log-scaled).

Installation

pip install taptoken

From source:

git clone https://github.com/fechad/TSTokenizer.git
cd TSTokenizer
pip install -e .

Quick start

from taptoken import tokenize, detokenize

series = [10.0, 12.5, 11.0, 14.0, 13.5, 16.0, 15.0]

tokens = tokenize(series)
# → [(pattern_id, [bias, delta, delta, ...]), ...]

reconstructed = detokenize(tokens, expected_length=len(series))

API

tokenize(values, vocabulary_type="base10", use_log_scale=True)

Parameter	Type	Description
values	list[float]	Input time series
vocabulary_type	"base10" | "base6"	Key type for pattern IDs (int vs str)
use_log_scale	bool	Apply log1p to magnitude deltas

Returns list[tuple[pattern_id, list[float]]].

---

detokenize(tokens, vocabulary_type="base10", use_log_scale=True, overlap_size=1, expected_length=None)

Parameter	Type	Description
tokens	output of tokenize	Token list to reconstruct
vocabulary_type	"base10" | "base6"	Must match the value used in tokenize
use_log_scale	bool	Must match the value used in tokenize
overlap_size	int	Overlap between chunks (default 1, matches encoder)
expected_length	int | None	Trim output to this length

Returns list[float].

Vocabulary types

Type	Pattern ID	Example key
base10	int	42
base6	str	"01215"

Both vocabularies encode the same set of tapper patterns; only the key format differs.

Development

pip install -e .
pytest tests/test_taptoken.py -v

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